Ant colony optimization-based multi-mode scheduling under renewable and nonrenewable resource constraints

• We apply ACO for solving the multi-mode resource-constrained scheduling problem.
• We adopt two levels of pheromones for ACO to search for activity and mode options.
• We propose two types of heuristic data and probabilities with computing algorithms.
• Consider nonrenewable resource-constraint and elitist-rank in updating pheromones.
• A SSG scheme is adopted to transform an ACO solution into a feasible schedule.

An ant colony optimization (ACO)-based methodology for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) considering both renewable and nonrenewable resources is presented. With regard to the MRCPSP solution consisting of activity sequencing and mode selection, two levels of pheromones are proposed to guide search in the ACO algorithm. Correspondingly, two types of heuristic information and probabilities as well as related calculation algorithms are introduced. Nonrenewable resource-constraint and elitist-rank strategy are taken into account in updating the pheromones. The flowchart of the proposed ACO algorithm is described, where a serial schedule generation scheme is incorporated to transform an ACO solution into a feasible schedule. The parameter-selection and the resultant performance of the proposed ACO methodology are investigated through a series of computational experiments. It is expected to provide an effective alternative methodology for solving the MRCPSP by utilizing the ACO theory.